{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision.transforms import transforms\n",
    "\n",
    "import torchvision.datasets\n",
    "\n",
    "mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=True,download=True,transform=transforms.ToTensor())\n",
    "mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=False,download=True,transform=transforms.ToTensor())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "(torch.Size([1, 28, 28]), torch.float32)"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature,label=mnist_train[0]\n",
    "feature.shape,feature.dtype"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "batch_size=256\n",
    "train_iter = torch.utils.data.DataLoader(mnist_train,batch_size=batch_size,shuffle=True,num_workers=4)\n",
    "test_iter = torch.utils.data.DataLoader(mnist_test,batch_size=batch_size,shuffle=False,num_workers=4)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "num_inputs=784\n",
    "num_outputs=10\n",
    "W=torch.tensor(np.random.normal(0,0.01,(num_inputs,num_outputs)),dtype=torch.float32)\n",
    "b=torch.zeros(num_outputs,dtype=torch.float32)\n",
    "W.requires_grad_(requires_grad=True)\n",
    "b.requires_grad_(requires_grad=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "def softmax(X):\n",
    "    X_exp=torch.exp(X)\n",
    "    partition = X_exp.sum(1,keepdim=True)\n",
    "    return X_exp/partition\n",
    "\n",
    "# X=torch.normal(0,1,(2,5))\n",
    "# X_prob=softmax(X)\n",
    "# X_prob,X_prob.sum(1)\n",
    "\n",
    "# 定义模型\n",
    "def net(X):\n",
    "    return softmax(torch.mm(X.view((-1,num_inputs)),W)+b)\n",
    "\n",
    "# 定义交叉熵损失函数\n",
    "def cross_entropy(y_hat,y):\n",
    "    return -torch.log(y_hat.gather(1,y.view(-1,1)))\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "def accuracy(y_hat,y):\n",
    "    return (y_hat.argmax(dim=1)==y).float().mean().item()\n",
    "\n",
    "def evaluate_accuracy(data_iter,net):\n",
    "    acc_sum,n=0.0,0\n",
    "    for X,y in data_iter:\n",
    "        acc_sum+=(net(X).argmax(dim=1)==y).float().sum().item()\n",
    "        n+=y.shape[0]\n",
    "        return acc_sum/n\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.7885, train acc 0.746, test acc 0.812\n",
      "epoch 2, loss 0.5698, train acc 0.814, test acc 0.797\n",
      "epoch 3, loss 0.5259, train acc 0.825, test acc 0.828\n",
      "epoch 4, loss 0.5022, train acc 0.832, test acc 0.832\n",
      "epoch 5, loss 0.4858, train acc 0.837, test acc 0.848\n",
      "epoch 6, loss 0.4745, train acc 0.840, test acc 0.844\n",
      "epoch 7, loss 0.4645, train acc 0.843, test acc 0.844\n",
      "epoch 8, loss 0.4586, train acc 0.844, test acc 0.848\n"
     ]
    }
   ],
   "source": [
    "from torch.optim.sgd import SGD\n",
    "\n",
    "num_epochs,lr=8,0.1\n",
    "\n",
    "def train_ch3(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,optimizer=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum,train_acc_sum,n=0.0,0.0,0\n",
    "        for X,y in train_iter:\n",
    "            y_hat = net(X)\n",
    "            l=loss(y_hat,y).sum()\n",
    "\n",
    "            if optimizer is not None:\n",
    "                optimizer.zero_grad()\n",
    "            elif params is not None and params[0].grad is not None:\n",
    "                for param in params:\n",
    "                    param.grad.data.zero_()\n",
    "            l.backward()\n",
    "            if optimizer is None:\n",
    "                for param in params:\n",
    "                    param.data -= lr * param.grad / batch_size\n",
    "                # SGD(params=params,lr=lr)\n",
    "            else:\n",
    "                optimizer.step()\n",
    "            train_l_sum += l.item()\n",
    "            train_acc_sum += (y_hat.argmax(dim=1)==y).sum().item()\n",
    "            n+=y.shape[0]\n",
    "        test_acc = evaluate_accuracy(test_iter,net)\n",
    "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'%(epoch+1,train_l_sum/n,train_acc_sum/n,test_acc))\n",
    "\n",
    "train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,batch_size,[W,b],lr)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "Parameter containing:\ntensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch import nn\n",
    "\n",
    "\n",
    "class SoftMaxNet(nn.Module):\n",
    "    def __init__(self, num_inputs,num_outputs):\n",
    "        super(SoftMaxNet,self).__init__()\n",
    "        self.linear = nn.Linear(num_inputs,num_outputs)\n",
    "\n",
    "    def forward(self,x):\n",
    "        view = x.view(-1,num_inputs)\n",
    "        y=self.linear(view)\n",
    "        return y\n",
    "\n",
    "softmaxnet = SoftMaxNet(num_inputs,num_outputs)\n",
    "loss = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(softmaxnet.parameters(),lr=0.1)\n",
    "from torch.nn import init\n",
    "\n",
    "init.normal_(softmaxnet.linear.weight,mean=0,std=0.01)\n",
    "init.constant_(softmaxnet.linear.bias,val=0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.0031, train acc 0.749, test acc 0.801\n",
      "epoch 2, loss 0.0022, train acc 0.814, test acc 0.809\n",
      "epoch 3, loss 0.0021, train acc 0.825, test acc 0.820\n",
      "epoch 4, loss 0.0020, train acc 0.832, test acc 0.832\n",
      "epoch 5, loss 0.0019, train acc 0.837, test acc 0.840\n",
      "epoch 6, loss 0.0019, train acc 0.840, test acc 0.836\n",
      "epoch 7, loss 0.0018, train acc 0.843, test acc 0.840\n",
      "epoch 8, loss 0.0018, train acc 0.846, test acc 0.852\n"
     ]
    }
   ],
   "source": [
    "Ws=torch.tensor(np.random.normal(0,0.01,(num_inputs,num_outputs)),dtype=torch.float32)\n",
    "bs=torch.zeros(num_outputs,dtype=torch.float32)\n",
    "Ws.requires_grad_(requires_grad=True)\n",
    "bs.requires_grad_(requires_grad=True)\n",
    "\n",
    "num_epochs,batch_size,lr=8,64,0.1\n",
    "\n",
    "train_ch3(softmaxnet,train_iter,test_iter,loss,num_epochs,batch_size,[Ws,bs],lr,optimizer)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib_inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 预测\n",
    "X,y = iter(test_iter).next()\n",
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]\n",
    "\n",
    "true_labels = get_fashion_mnist_labels(y.numpy())\n",
    "pred_labels = get_fashion_mnist_labels(softmaxnet(X).argmax(dim=1).numpy())\n",
    "titles = [true+'\\n'+pred for true,pred in zip(true_labels,pred_labels)]\n",
    "matplotlib_inline.backend_inline.set_matplotlib_formats('svg')\n",
    "\n",
    "_,figs = plt.subplots(1,len(X[:9]),figsize=(16,16))\n",
    "for f, img, lbl in zip(figs,X[:9],titles[:9]):\n",
    "    f.imshow(img.view((28,28)).numpy())\n",
    "    f.set_title(lbl)\n",
    "    f.axes.get_xaxis().set_visible(False)\n",
    "    f.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "fig=plt.gcf()\n",
    "fig.savefig('./2.svg')"
   ],
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    "collapsed": false
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